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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Simetris Jurnal ULTIMA Computing
Wicaksana, Haryas Subyantara
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Computer Science & IT Control & Systems Engineering Engineering

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RANCANG BANGUN HUMAN ACTIVITY RECOGNIZER SEBAGAI CLASSIFIER SINYAL GEMPA BUMI DAN AKTIVITAS MANUSIA PADA AKSELEROMETER SMARTPHONE Wicaksana, Haryas Subyantara; Nugroho, Hapsoro Agung
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 1 (2020): JURNAL SIMETRIS VOLUME 11 NO 1 TAHUN 2020
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (934.139 KB) | DOI: 10.24176/simet.v11i1.3035

Abstract

Akselerometer pada smartphone dapat merekam sinyal gempa bumi guna mendukung mitigasi kebencanaan di Indonesia. Aktivitas manusia menghasilkan noise yang cukup signifikan terhadap data akselerometer pada smartphone. Human Activity Recognizer (HAR) diimplementasikan memilah sinyal aktivitas manusia dari sinyal gempa bumi yang direkam akselerometer smartphone. Penelitian ini bertujuan untuk mengklasifikasikan sinyal percepatan linier aktivitas manusia dengan sinyal percepatan  gempa bumi pada akselerometer smartphone Android melalui Human Activity Recognizer (HAR) berbasis algoritma K-Nearest Neighbor (K-NN). Metode penelitian terdiri dari penghimpunan data, pra pengolahan data, segmentasi data, ekstraksi ciri, dan klasifikasi. Berdasarkan hasil pengujian HAR sebagai classifier sinyal gempa bumi dan sinyal aktivitas manusia menggunakan algoritma K-NN secara umum mampu memilah sinyal akselerometer smartphone akibat aktivitas manusia dan sinyal gempa bumi dengan rentang akurasi 66,9% hingga 100%. Algoritma tipe Fine K-NN memiliki akurasi tertinggi sebesar 100%.
Solar Radiation Intensity Imputation in Pyranometer of Automatic Weather Station Based on Long Short Term Memory Pahlepi, Richat; Soekirno, Santoso; Wicaksana, Haryas Subyantara
Ultima Computing : Jurnal Sistem Komputer Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3348

Abstract

Automatic Weather Station (AWS) experienced problems in the form of component damage and communication system failure, resulting in incomplete parameter data. Component damage also occurs in pyranometers. Decreased pyranometer performance results in deviations, uncertainty in measuring solar radiation intensity, and data gaps. Data imputation is one solution to minimize measurement deviations and the occurrence of missing AWS pyranometer data. This research aims to design and analyze the accuracy performance of the multisite AWS pyranometer solar radiation intensity data imputation model when a data gap occurs. This research attempts to utilize the spatio-temporal relationship of multisite AWS solar radiation intensity in the imputation model. Long-Short Term Memory (LSTM) algorithm is used as an estimator in the multisite AWS pyranometer network. Data imputation modeling stage includes data collection, data pre-processing, creating missing data scenarios, LSTM design and model testing. Overall, LSTM-based imputation model has ability of filling gap data on AWS Cikancung pyranometer with maximum missing sequence of 12 hours. Imputation model has MAPE 1.76% - 5.26% for missing duration 30 minutes-12 hours. It still it meet WMO requirement for solar radiation intensity measurement with MAPE<8%.
Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
Ultima Computing : Jurnal Sistem Komputer Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Abstract

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.
Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province Soekirno, Santoso; Ananda, Naufal; Wicaksana, Haryas Subyantara; Yulizar, David; Prabowo, Muhammad Agung; Adi, Suko Prayitno; Santoso, Bayu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1878-1887

Abstract

Drought Early Warning System (DEWS) is an effort to disseminate early warning information based on climate and hydrology aspects. The DEWS design uses ARIMA, MLP, and hybrid ARIMA-MLP models to predict drought based on SPI for 1, 3, and 6 months. Predictions were made using ERA5 monthly rainfall data from 1981-2022 corrected based on observation data on 9 grids of observation rain gauges in Banten Province. The design of the ARIMA model is determined by selecting the combination of p and q parameters with the lowest AIC value, while the MLP architecture is determined by referring to the study literature and by trial and error testing. ARIMA models and hybrid models are not able to follow actual data fluctuations and have high error values in both SPI1, SPI3, and SPI6, so they are not recommended in this study. The MLP model has the best prediction ability, namely in SPI6 prediction with NSE value reaching >0.5 and RMSE value.
Co-Authors Adi, Suko Prayitno Ananda, Naufal Budiawan, Irvan Deddy Kurniadi, Deddy Hapsoro Agung Nugroho Muhammad Agung Prabowo, Muhammad Agung Mukhlish, Faqihza Pahlepi, Richat Ryan, Muhammad Santoso, Bayu Soekirno, Santoso Yulizar, David